Proliferation-Linked Apoptosis of Adoptively Transferred T Cells after IL-15 Administration in Macaques
Bibliographic record
Abstract
The adoptive transfer of antigen-specific effector T cells is being used to treat human infections and malignancy. T cell persistence is a prerequisite for therapeutic efficacy, but reliably establishing a high-level and durable T cell response by transferring cultured CD8(+) T cells remains challenging. Thus, strategies that promote a transferred high-level T cell response may improve the efficacy of T cell therapy. Lymphodepletion enhances persistence of transferred T cells in mice in part by reducing competition for IL-15, a common γ-chain cytokine that promotes T cell memory, but lymphodepleting regimens have toxicity. IL-15 can be safely administered and has minimal effects on CD4(+) regulatory T cells at low doses, making it an attractive adjunct in adoptive T cell therapy. Here, we show in lymphoreplete macaca nemestrina, that proliferation of adoptively transferred central memory-derived CD8(+) effector T (T(CM/E)) cells is enhanced in vivo by administering IL-15. T(CM/E) cells migrated to memory niches, persisted, and acquired both central memory and effector memory phenotypes regardless of the cytokine treatment. Unexpectedly, despite maintaining T cell proliferation, IL-15 did not augment the magnitude of the transferred T cell response in blood, bone marrow, or lymph nodes. T cells induced to proliferate by IL-15 displayed increased apoptosis demonstrating that enhanced cycling was balanced by cell death. These results suggest that homeostatic mechanisms that regulate T cell numbers may interfere with strategies to augment a high-level T cell response by adoptive transfer of CD8(+) T(CM/E) cells in lymphoreplete hosts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".